Generally, for solving an ill-posed problems, we need to incorporate the prior information about the signal to be reconstructed. Traditionally these priors are either analytically derived or hand-crafted based on the observations. For example, sparsity of image gradients [1]}, sparsity of coefficients in wavelet and DCT domain [2]} etc. have been used for solving inverse imaging problems. However, the underlying data distribution may not precisely follow these analytic priors leading to poor solutions in challenging scenarios. Dictionary learning [3]} methods being data driven are an improvement over these analytic priors. However, being limited by patch size they cannot account for long range dependencies which are necessary for handling global multiplexing in case of compressive image reconstruction.
